CN103414667B - A kind of OFDM adaptive channel estimation method based on two-dimensional discrete pilot tone - Google Patents

Abstract

The invention discloses a kind of OFDM adaptive channel estimation method based on two-dimensional discrete pilot tone, at transmitting terminal, insert two-dimensional discrete interpolation pilot tone in each ofdm symbol, and produce training pilot tone, position and the numerical value of training pilot tone and interpolation pilot tone are all known to receiving terminal.To each OFDM symbol, receiving terminal is under the condition of known FDI interpolation coefficient, first one dimension FDI is carried out to the channel estimation value of interpolation pilot tone, FDI Output rusults is carried out interpolation as virtual pilot frequency to training pilot tone, calculate its channel estimation value, the channel estimation value of the training pilot tone obtained in conjunction with direct estimation is trained tap coefficient.Namely all training pilot tones in each OFDM symbol obtain interpolation coefficient by tap coefficient after all training, thus realize the channel estimating to data.The present invention is applicable to the ofdm communication system adopting two-dimensional discrete pilot tone, can carry out channel estimating when the characteristic the unknown of channel time directional statistics, and can adaptive tracing fast fading channel.

Description

An OFDM Adaptive Channel Estimation Method Based on Two-Dimensional Scattered Pilots

technical field

The invention belongs to the technical field of wireless communication, and more specifically relates to an OFDM adaptive channel estimation method based on two-dimensional scattered pilots.

Background technique

OFDM (Orthogonal Frequency Division Multiplexing, Orthogonal Frequency Division Multiplexing) is a special multi-carrier modulation technology, which has natural advantages in combating multipath fading and is very suitable for high-speed data transmission. Therefore, OFDM has been widely used in modern wireless broadband access systems, such as DAB (Digital Audio Broadcasting, Digital Audio Broadcasting), DVB (Digital Video Broadcasting, Digital Television Broadcasting), LTE (Long Term Evolution, Long Term Evolution), WiFi, WiMAX (Worldwide Interoperability for Microwave Access, ie Global Microwave Interconnection Access), etc. In a wireless OFDM system, the multipath effect and the Doppler effect will cause the wireless channel to have frequency-domain selective fading and time-selective fading respectively, which will have a bad impact on the receiver using coherent demodulation and degrade the system performance. Therefore, a high-performance channel estimation method is required to accurately obtain channel information and eliminate the influence of multipath channels through channel equalization.

In the existing OFDM system, the input data at the transmitting end undergoes channel coding, mapping, subcarrier allocation, and pilot insertion, and then uses OFDM modulation, that is, IFFT (Inverse Fast Fourier Transform, Inverse Fast Fourier Transform) transformation. In order to eliminate the influence of ISI (InterSymbolInterference, intersymbol interference) and ICI (InterCarrierInterference, intercarrier interference), OFDM modulation output data needs to be added with CP (CyclicPrefix, cyclic prefix). The transmitted signal reaches the receiving end through the channel. The processing process at the receiving end is basically the opposite of that at the transmitting end, except that channel estimation and channel equalization are added. Channel estimation is to estimate channel state information (CSI: ChannelStateInformation), such as channel impulse response (CIR: ChannelImpulseResponse), channel frequency domain response (CFR: ChannelFrequencyResponse), etc. Channel equalization is to use the CSI estimated by the channel to eliminate the influence of the multipath channel. Therefore, the performance of channel estimation is directly related to the performance of channel equalization, which in turn affects the performance of the entire OFDM system.

In the OFDM system, the traditional channel estimation can use two one-dimensional channel estimation cascading and other methods. The concatenation of two one-dimensional channel estimates is to concatenate one-dimensional time direction interpolation (TDI: TimeDirectionInterpolation) and one-dimensional frequency direction interpolation (FDI: FrequencyDirectionInterpolation). One-dimensional interpolation algorithms mainly include methods such as polynomial interpolation and digital interpolation filter interpolation. Polynomial interpolation also includes linear interpolation, second-order Gaussian interpolation, cubic Lagrangian interpolation, cubic spline interpolation, etc. Digital interpolation filter interpolation includes low-pass sinc plus window function interpolation and so on. However, when the FDI interpolation coefficients are known and the above-mentioned one-dimensional interpolation algorithm is applied to the high-speed mobile OFDM system for TDI, there are defects. It can be seen from the Doppler effect that the high-speed mobile OFDM system will generate a large Doppler frequency, causing fast fading of the channel. The above-mentioned methods all have deficiencies in combating fast fading channels. For example, although polynomial interpolation does not require the statistical characteristics of the channel, it is only suitable for slow fading channels. Moreover, the interpolation coefficients of the polynomial interpolation are fixed, which cannot track time-varying channels. Although digital interpolation filter interpolation can be applied to fast fading channels, it requires the statistical characteristics of the channel, such as the maximum Doppler frequency of the channel, which is often unknown in practice and needs to be estimated by other methods, adding an algorithm If you want to make it adaptively track channel changes, the complexity of the algorithm will be greatly increased.

Contents of the invention

The purpose of the present invention is to overcome the deficiencies of the prior art and provide a low-complexity OFDM adaptive channel estimation method based on two-dimensional scattered pilots, which can adaptively track fast fading channels when the FDI interpolation coefficients are known.

For realizing the foregoing invention object, the present invention is based on the OFDM adaptive channel estimation method of two-dimensional scattered pilot, it is characterized in that comprising the following steps:

S1: The transmitting end inserts two-dimensional discrete interpolation pilots into each OFDM symbol, and the two-dimensional discrete interpolation pilots are evenly distributed in the time-frequency two-dimensional dimension, and the period in the time direction is D _t , and the period in the frequency direction is D _f ; The position and value of the interpolated pilot are known to the receiving end;

S2: Generate training pilots in each OFDM symbol, remember that the lth, l=0, 1, 2, ... OFDM symbols include N _l > 0 training pilots, N _l is the number of preset training pilots number; the position and value of the training pilot are also known to the receiving end;

S3: The receiving end sequentially receives the sent OFDM symbols, and estimates the channel estimation value at the interpolated pilot in the OFDM symbol k _in is the subcarrier corresponding to the interpolation pilot in the lth OFDM symbol;

S4: Using the known FDI interpolation coefficients, first estimate the channel value of the interpolated pilot Perform FDI, and the interpolation result is k is all subcarriers included in the OFDM symbol;

S5: Set the initial OFDM symbol for data channel estimation, and perform data channel estimation on the symbol and subsequent OFDM symbols, including steps:

S5.1: Estimate the channel estimate at the training pilot in the OFDM symbol k _m , m=0, 1, ..., N _l -1 is the subcarrier corresponding to the training pilot in the lth OFDM symbol;

S5.2: Train the N _l training pilots of the l-th OFDM symbol in turn, and calculate the error signal of the m-th training pilot $e_l\left[\mathrm{no}\right]={\tilde{h}}^{\′}[l,k_m]-\underset{i=0}{\overset{N_f-1}{\mathrm{\Σ}}}{\hat{w}}_i^{*}\left[\mathrm{no}\right]\tilde{h}[l+m_1{\mathrm{D.}}_t-i,k_m],$ where the superscript ^* indicates conjugation; i=0,..., N _f -1 is the tap coefficient, which is obtained during the training of the n-1th training pilot; N _f =Q _t D _t +1, Q _t =M ₁ +M ₂ +1, M ₁ and M ₂ are set parameters, M ₁ ≥ 0, M ₂ +1 ≥ 1; when l+M ₁ D _t -i, when the OFDM symbol corresponding to 0≤i≤N _f -1 does not exist,

S5.3: Update tap coefficients ${\hat{w}}_i[\mathrm{no}+1]={\hat{w}}_i\left[\mathrm{no}\right]+\mathrm{\ρ}\tilde{h}[l+m_1{\mathrm{D.}}_t-i,k_m]e_l^{*}\left[\mathrm{no}\right],$ i=0,..., N _f -1, where ρ is the preset step size, i=0,..., _Nf -1 is the tap coefficient of the n+1th training pilot, when the data channel estimation is performed on the OFDM symbol for the first time, the tap coefficients corresponding to the 0th training pilot are all 0; When the N _l training pilots are all trained, output the interpolation coefficient j=-M ₁ D _t ,-M ₁ D _t +1,..., (M ₂ +1)D _t -1;

S5.4: According to the interpolation coefficient c _l [j], calculate the channel estimation value of the data in the l-th OFDM symbol as: k _d is the subcarrier corresponding to the data in the lth OFDM symbol.

The invention creates an OFDM adaptive channel estimation method based on two-dimensional scattered pilots. Insert two-dimensional discrete interpolation pilots into OFDM symbols at the transmitting end, and generate training pilots, wherein the two-dimensional discrete interpolation pilots are evenly distributed in two dimensions of time-frequency, and the training pilots are randomly distributed along the frequency direction. For each OFDM symbol, the receiving end trains the tap coefficients of the interpolator according to the channel information reference provided by the training pilot, and uses the trained tap coefficients to improve the accuracy of channel estimation.

The present invention is applicable to an OFDM communication system adopting a two-dimensional discrete interpolation pilot structure, and performs one-dimensional time direction interpolation (TDI: TimeDirectionInterpolation) under the condition of known FDI interpolation coefficients. The present invention has the following beneficial effects:

(1) By using training pilots, channel estimation can be performed when the statistical characteristics of the channel time direction are unknown;

(2) Since each OFDM symbol contains training pilots, the adaptive tracking of the channel is realized by training and updating the interpolation coefficient of each OFDM symbol;

(3) It is shown by simulation that the present invention can adaptively match the maximum Doppler frequency of the channel and meet the needs of high-speed mobile OFDM systems.

Description of drawings

Fig. 1 is the structural representation of the OFDM system that adopts the OFDM adaptive channel estimation method based on the two-dimensional scattered pilot frequency of the present invention;

Fig. 2 is a schematic structural diagram of a specific embodiment of data and pilot in the present invention;

Fig. 3 is a kind of specific embodiment flowchart of the OFDM adaptive channel estimation method based on the two-dimensional scattered pilot frequency at the receiving end of the present invention;

Fig. 4 is a schematic diagram comparing the Doppler domain response characteristics between the present invention and the prior art;

Fig. 5 is a comparative schematic diagram of the convergence characteristics of the present invention under different step lengths;

Fig. 6 is a schematic diagram of the MSE performance comparison between the present invention and the prior art under different SNRs;

Fig. 7 is a schematic diagram of the MSE lower bound comparison between the present invention and the prior art at different Doppler frequencies;

Fig. 8 is a comparison simulation of bit error performance between the present invention and the prior art.

detailed description

Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

FIG. 1 is a schematic structural diagram of an OFDM system using the OFDM channel estimation method based on two-dimensional scattered pilots of the present invention. As shown in Fig. 1, the main idea of the present invention is to insert two-dimensional discrete interpolation pilots at the transmitting end and generate training pilots. The generation of training pilots includes two ways: inserting known training information and decision feedback to generate training pilots. Wherein, the role of the interpolation pilot is the same as that of the prior art, and is used to interpolate OFDM symbols; and the role of the training pilot is to train interpolation coefficients. Like the interpolation pilot, the position and value of the training pilot are known to the receiving end, so the present invention can directly use the known training pilot to train the interpolation coefficient at the receiving end without knowing the channel time direction statistical properties.

Fig. 2 is a schematic structural diagram of a specific implementation manner of data and pilot in the present invention. As shown in Figure 2, each row is an OFDM symbol, and the applicable object of the present invention is an OFDM communication system adopting a two-dimensional discrete interpolation pilot structure, that is, the interpolation pilot is periodically uniformly distributed in the two-dimensional time-frequency, and is recorded in the time direction The period is D _t , and the period in the frequency direction is D _f . The present invention is implemented on the output of FDI, and the FDI interpolation coefficients are known. The receiving end first performs FDI according to the channel estimation value of the interpolation pilot, obtains the interpolation results corresponding to all subcarriers included in the OFDM symbol to which the interpolation pilot belongs, and uses each interpolation result as a virtual pilot. It can be seen that the virtual pilot is not actually transmitted, but is obtained by the receiving end through FDI on the interpolated pilot. As for the training pilots, in each OFDM symbol, the positions and numbers of the training pilots may be different. The positions of the training pilots are preferably randomly distributed on the frequency axis, and the random rules are known to the receiving end, so that all interpolation coefficients can be fully trained. The number of training pilots is preset and needs to be determined according to the convergence characteristics of the interpolation coefficients. Note that the number of training pilots contained in the lth, l=0, 1, 2, ... OFDM symbols is N _l > 0, and the corresponding subcarrier position is marked as k _m , m = 0, 1, ..., N _l - 1.

In the prior art, channel estimation based on pilot interpolation at the receiving end is divided into two steps: the first step is to estimate the CFR at the interpolated pilot; the second step is to use an interpolation algorithm to obtain the interpolation coefficient, and then estimate the CFR at the data point. In the present invention, the estimated value of the channel at the interpolation pilot is k _in is the subcarrier corresponding to the interpolation pilot in the OFDM symbol. FDI is performed on the interpolated pilot CFR according to the known FDI interpolation coefficient, and the interpolation result at the kth subcarrier is obtained as 0≤k≤T-1, T is the effective number of subcarriers included in the OFDM symbol. The channel estimation value (ie, CFR) at the data (l, k _d ) can be obtained by formula (1), where k _d is the subcarrier corresponding to the data in the OFDM symbol.

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ]</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, M ₁ and M ₂ are set parameters, M ₁ ≥ 0, M ₂ +1 ≥ 1; when lj, the OFDM symbol corresponding to -M ₁ D _t ≤ j ≤ (M ₂ +1)D _t -1 is not when present,

It can be seen that when performing interpolation channel estimation on the data (l, k _d ), the virtual pilot is used to perform TDI interpolation, and the CFR used is OFDM symbols from l-(M ₂ +1)D _t +1 to l+M ₁ D _t , the subcarrier is the CFR at each virtual pilot present on k _d . As shown in Figure 2, there is a virtual pilot on each subcarrier in the OFDM symbol to which the interpolated pilot belongs, D _t =4, here M ₁ =1, M ₂ +1 =1, so -4 ≤ j ≤ 3. For the data Z, assuming that the OFDM symbol sequence number where it is located is l and the subcarrier is k _d , then l-3≤lj≤l+4. Then, when performing interpolation channel estimation on the data Z, the CFR adopted is the FDI output result included in the box in Fig. 2 , that is, the virtual pilot. The size of the two parameters M ₁ and M ₂ determines the number of virtual pilots used during interpolation. The larger the parameter value, the more virtual pilots are used, and the more accurate the channel estimation value of the obtained data is, but the calculation is complicated will also increase accordingly. In practical applications, it can be determined as required.

It can be seen that when the FDI interpolation coefficients are known, It is only determined by the channel estimation algorithm at the interpolated pilot. Therefore, when the same estimation method is used at the interpolation pilot, the channel estimate at the data It is only related to the interpolation coefficient c _l [j]. There are many methods for calculating c _l [j] in the prior art, such as polynomial interpolation and digital interpolation filter interpolation. Polynomial interpolation also includes linear interpolation, second-order Gaussian interpolation, and cubic Lagrange interpolation. Digital interpolation filter interpolation also includes adding low-pass sinc plus window function interpolation, etc., which are recorded as complex coefficient LPS (Low-PassSinc). However, in the present invention, the interpolation coefficient c _l [j] can be easily obtained through the training pilot, and the statistical characteristics of the channel time direction are not required, the complexity is not high, and the time-varying channel can be adaptively tracked. The realization thought of the present invention is described below:

In the present invention, the channel estimation value at the l-th OFDM symbol and the _m -th training pilot (l, km ) can also be obtained by formula (1), namely:

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; l</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ]</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

structure ${\hat{h}}^{\&prime;}[l,k_m]=\underset{i=0}{\overset{N_f-1}{\mathrm{\&Sigma;}}}{\hat{w}}_i^{*}\left[\mathrm{no}\right]\hat{h}[l+m_1{\mathrm{D.}}_t-i,k_m];$ Where N _f =Q _t D _t +1, Q _t =M ₁ +M ₂ +1, M ₁ and M ₂ are set parameters, M ₁ ≥0, M ₂ +1≥1; Represents the m-th training pilot of the l-th OFDM symbol; the superscript ^* represents the conjugate; i=0,..., N _f -1 is the tap coefficient; similarly, when the OFDM symbol corresponding to l+M ₁ D _t -i, 0≤i≤N _f -1 does not exist, This establishes the interpolation coefficient c _l [j] and the tap coefficient Relationship. When the N _l training pilots of the l-th OFDM symbol are all trained, that is It can be seen that with the present invention, when the receiving end knows the channel estimation values of the interpolation pilot and the training pilot and the FDI interpolation coefficient, the interpolation coefficient can be obtained.

FIG. 3 is a flow chart of a specific embodiment of the OFDM adaptive channel estimation method based on two-dimensional scattered pilots at the receiving end of the present invention. As shown in Figure 3, the OFDM channel estimation method carried out at the receiving end in the present invention comprises the following steps:

S301: The receiving end receives OFDM symbols in sequence, estimates the frequency domain response of the channel at each interpolation pilot, and obtains the channel estimation value at each interpolation pilot The channel estimation algorithm at the pilot frequency includes LS algorithm, MMSE algorithm and so on. Because the LS algorithm is simple, has good performance, and does not require channel statistics, a compromise between performance and complexity is reached, so the channel estimation at the pilot frequency usually uses the LS algorithm. In this embodiment, the channel estimation at the interpolation pilot adopts the LS algorithm, and the obtained results are as follows:

Where: Y[l, k _in ] represents the received interpolated pilot value, and X[l, k _in ] represents the interpolated pilot value mapped by the transmitting end.

S302: According to the estimated channel value at the interpolated pilot obtained in step S301 Using the known FDI interpolation coefficients to perform one-dimensional FDI, the interpolation result at the kth subcarrier is obtained as 0≤k≤T-1, T is the effective number of subcarriers included in the OFDM symbol.

Set the initial OFDM symbol for data channel estimation, perform training pilot estimation on this symbol and subsequent OFDM symbols, and then obtain data channel estimation.

S303: For each OFDM symbol, use the channel estimation algorithm at the pilot to estimate the frequency domain response of the channel at each training pilot, and obtain the channel estimation value at each training pilot In this embodiment, the LS algorithm is also used, and the results are as follows:

Where: Y[l, _km ] represents the received training pilot value, and X[l, _km ] represents the training pilot value mapped by the transmitting end.

Train the N1 training pilots of the _l -th OFDM symbol in turn to obtain interpolation coefficients, and then perform data channel estimation for each OFDM symbol according to the interpolation coefficients. The training steps include S304 to S308.

S304: Calculate the error signal of the mth training pilot of the lth OFDM symbol $e_l\left[\mathrm{no}\right]={\tilde{h}}^{\&prime;}[l,k_m]-\underset{i=0}{\overset{N_f-1}{\mathrm{\&Sigma;}}}{\hat{w}}_i^{*}\left[\mathrm{no}\right]\tilde{h}[l+m_1{\mathrm{D.}}_t-i,k_m],$ in $\mathrm{no}=\underset{\mathrm{the\; y}=0}{\overset{l-1}{\mathrm{\&Sigma;}}}{N}_{\mathrm{the\; y}}+m;$ The superscript ^* indicates conjugation; i=0,..., N _f -1 is the tap coefficient, which is obtained during the n-1th training pilot training; N _f =Q _t D _t +1, Q _t =M ₁ +M ₂ +1, M ₁ , M ₂ is the set parameter, M ₁ ≥ 0, M ₂ +1 ≥ 1; i=0, ..., N _f -1 is the OFDM symbol from l-(M ₂ +1)D _t +1 to l+M ₁ D _t , the subcarrier is k _m and the FDI interpolation is obtained through steps S301 and S302 The result, the virtual pilot;

S305: Updating the tap coefficient ${\hat{w}}_i[\mathrm{no}+1]={\hat{w}}_i\left[\mathrm{no}\right]+\mathrm{\&rho;}\tilde{h}[l+m_1{\mathrm{D.}}_t-i,k_m]e_l^{*}\left[\mathrm{no}\right],$ i=0,...,N _f -1, where ρ is a preset step size, and the value of ρ depends on the system's requirements in terms of convergence rate, channel estimation accuracy, signal-to-noise ratio, and channel parameters; i=0, ..., N _f -1 is the tap coefficient of the n+1th training pilot. When data channel estimation is performed on data OFDM symbols for the first time, the tap coefficients corresponding to the 0th training pilot are all 0. That is, assuming that the OFDM symbol sequence number of the first data channel estimation is l ₀ , the tap coefficient corresponding to the 0th training pilot i=0, . . . , _Nf -1.

S306: Determine whether all the training pilots in the current OFDM symbol have been trained, if not, go to step S307, if all the training is done, go to step S308.

S307: Take the next training pilot, that is, m=m+1, return to step S304 to train the next training pilot.

S308: Obtained according to training i=0,..., N _f -1, output interpolation coefficient j=-M ₁ D _t , -M ₁ D _t +1, . . . , (M ₂ +1)D _t -1.

S309: According to the interpolation coefficient c _l [j] obtained in step S308, calculate the channel estimation value of the data in the lth OFDM symbol as: $\hat{h}[l,k_d]=\underset{j={-m}_1{\mathrm{D.}}_t}{\overset{(m_2+1){\mathrm{D.}}_t-1}{\mathrm{\&Sigma;}}}{c}_l\left[j\right]\tilde{h}[l-j,k_d],$ in $\tilde{h}[l-j,k_d]$ is the OFDM symbol l-(M ₂ +1)D _t +1 to l+M ₁ D _t , the subcarrier is k _d and the FDI interpolation result obtained through steps S301 and S302, that is, the virtual pilot. The channel estimation value of the output data is used for data recovery.

It can be seen that the present invention needs to use the virtual pilots contained in the l-(M ₂ +1)D _t to l+M ₁ D _t OFDM symbols during application, so in practical applications, the receiving end needs a buffer area to temporarily store Q _t D _t +1 OFDM symbols including the lth OFDM symbol. When channel estimation is performed on OFDM symbol l, if l belongs to the first 0th to (M ₂ +1)D _t -1th OFDM symbols, the OFDM symbols before OFDM symbol l that need to be used in training coefficients and If not all of them exist, the error of the obtained data channel estimation value is large. Therefore, in practical applications, the channel estimation starting OFDM symbol may not be the 0th to (M ₂ +1)D _t -1th OFDM symbol, but may be set to the (M ₂ +1)D _tth OFDM symbol, for The previous OFDM symbols do not perform data channel estimation, but start to estimate the channel estimation value at the training pilot from the (M ₂ +1)D _tth IFDM symbol, so as to obtain the channel estimation value of the data, that is, the data channel estimation The starting symbol is the (M ₂ +1)D _t th OFDM symbol. In this case, in order to avoid the loss of useful data, the OFDM symbols in the 0th to (M ₂ +1)D _t -1 OFDM symbols do not carry useful data information, and can be empty data, that is, the corresponding The subcarriers are not loaded with data, or are filled with other data that do not carry useful information. Of course, in practical applications, the starting OFDM symbol for data channel estimation can be determined according to actual needs.

It can be seen that, by using the training pilot, the present invention can conveniently obtain the TDI interpolation coefficient and complete the channel estimation of the data under the condition that the FDI interpolation coefficient is known and the statistical characteristics of the channel time direction are unknown. And since each OFDM symbol contains training pilots, the adaptive tracking of the channel is realized by training and updating the interpolation coefficients of each OFDM symbol.

Example

A specific implementation case of the present invention in the DVB-H system is introduced below, and a simulation result diagram is given. System simulation parameters: the number of FFT (FastFourierTransform, Fast Fourier Transform) points is 8192, the CP mode is 14, the mapping mode is 16QAM (QuadratureAmplitudeModulation, quadrature amplitude modulation), and the simulation system uses convolutional coding with a code rate of 23. The simulation uses the COST207TU6 channel model, and Table 1 shows the power delay spectrum of the COST207TU6 channel model.

Table 1

The pilot structure adopted by the DVB-H system is a two-dimensional scattered pilot. In order to perform a fair performance comparison with other one-dimensional interpolation algorithms, the maximum multipath delay is used on FDI Interpolation by a digital interpolation filter of order N' _f =25, where T _s is the OFDM symbol period.

In the OFDM adaptive channel estimation method based on two-dimensional scattered pilots of the present invention, two-dimensional scattered pilots are used as interpolation pilots and continuous pilots are used as training pilots. For each OFDM symbol, N _l =177.

Fig. 4 is a schematic diagram of comparing Doppler domain response characteristics between the present invention and the prior art. Simulation parameters: SNR (SignalNoiseRate, signal-to-noise ratio) is 20dB, Q _t =4. It can be seen from Fig. 4 that the bandwidths of linear interpolation and three-time Lagrange interpolation are fixed, which are about 20Hz and 50Hz respectively. However, the present invention can adaptively adjust its own bandwidth to match the maximum Doppler frequency set in the channel emulator, thereby meeting the needs of high-speed mobile OFDM systems.

Fig. 5 is a schematic diagram of the comparison of the convergence characteristics of the present invention under different step lengths. The simulation results shown in Figure 5 can provide a reference for selecting the adaptive iteration step size. Each pair of training pilots is trained as an iteration. Simulation parameters: the maximum Doppler frequency is 100 Hz, the SNR is 20 dB, and Q _t =4. For each step size, the mean MSE (Mean Square Error) is obtained by averaging the results of 200 independent trials. As shown in FIG. 5 , as the step size ρ increases, the convergence speed of the algorithm proposed by the present invention will become faster. But a large step size ρ will cause the algorithm to be unstable. Therefore, the value of the step size ρ needs to take into account the convergence speed and stability of the algorithm. In the subsequent simulation of this embodiment, the step size ρ=0.005 is selected.

FIG. 6 is a schematic diagram of the MSE performance comparison between the present invention and the prior art under different SNRs. Simulation parameters: the maximum Doppler frequency is 100Hz. The MSE here is obtained by averaging the 1000 OFDM symbols after convergence. LPS (Low-PassSinc, Low-Pass Sinc) is a low-pass sinc interpolation algorithm with a KAISER window, and TDI means that the LPS algorithm is applied to interpolate in the time direction. As shown in Figure 6, MSE gradually decreases with the increase of SNR, but there is a lower bound of MSE. It can also be seen that the MSE performance of the present invention is not sensitive to the value of Q _t , so a small Q _t value can be selected in practical applications to greatly reduce the complexity of the algorithm.

Fig. 7 is a schematic diagram of comparing the MSE lower bounds of the present invention and the prior art at different Doppler frequencies. Simulation parameters: SNR=30dB. As shown in Figure 6, when SNR=30dB, the average MSE of several methods has reached the lower bound, basically no change. Figure 7 simulates the performance of the average MSE lower bound at different Doppler frequencies. As shown in Figure 7, the performance of the present invention is better than that of LPS-TDI. Moreover, when simulating LPS-TDI, it has been assumed that the maximum Doppler frequency is known, but this needs to be estimated additionally in practice, which will increase the complexity of LPS-TDI.

Fig. 8 is a comparison simulation of bit error performance between the present invention and the prior art. Simulation parameters: the maximum Doppler frequency is 120Hz, and Viterbi decoding and channel equalization technology are used. For details of channel equalization technology, please refer to: G.Liu, S.V.Zhidkov, H.Li, L.Zeng, and Z.Wang, "Low -complexity iterative equalization for symbol-reconstruction based OFDM receivers over doubly selective channels," IEEE Trans. Broadcast., vol.58, no.3, pp.390–400, Sept.2012. As shown in Figure 8, the BER (BitErrorRate, bit error rate) of the present invention is slightly worse than the ideal channel estimation method, but better than the three algorithms of LPS-TDI, linear interpolation and Lagrangian interpolation.

Although the illustrative specific embodiments of the present invention have been described above, so that those skilled in the art can understand the present invention, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, As long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

Claims (7)

1., based on an OFDM adaptive channel estimation method for two-dimensional discrete pilot tone, it is characterized in that comprising the following steps:

S1: transmitting terminal inserts two-dimensional discrete interpolation pilot tone in each OFDM symbol, and two-dimensional discrete interpolation pilot tone is uniformly distributed at time-frequency two-dimensional, the cycle on note time orientation is D _t, the cycle in frequency direction is D _f; The position of interpolation pilot tone and numerical value are known for receiving terminal;

S2: produce training pilot tone in each ofdm symbol, remember l, l=0,1,2 ... individual OFDM symbol comprises N _l> 0 trains pilot tone, N _lfor the number of default training pilot tone; Position and the numerical value of training pilot tone are also known for receiving terminal;

S3: receiving terminal receives the OFDM symbol of transmission successively, estimates the channel estimation value obtaining interpolation pilot tone place in OFDM symbol k _init is the subcarrier that in l OFDM symbol, interpolation pilot tone is corresponding;

S4: utilize known FDI interpolation coefficient, FDI represents one-dimensional frequency directional interpolation, first to the channel estimation value of interpolation pilot tone carry out FDI, the interpolation result obtaining a kth subcarrier place is 0≤k≤T-1, T is the number of subcarriers comprised in OFDM symbol;

S5: the initial OFDM symbol of setting data channel estimating, carries out data channel estimation to this symbol and OFDM symbol afterwards thereof, comprises step:

S5.1: estimate to obtain the channel estimation value of training pilot tone place in OFDM symbol k _m, m=0,1 ..., N _l-1 is the subcarrier of training pilot tone corresponding in l OFDM symbol;

S5.2: successively to the N of l OFDM symbol _lindividual training pilot tone is trained, and calculates the error signal of m training pilot tone $e_l\&lsqb;n\&rsqb;={\tilde{H}}^{\&prime;}\&lsqb;l,k_m\&rsqb;-\underset{i=0}{\overset{N_f-1}{\&Sigma;}}{\hat{w}}_i^{*}\&lsqb;n\&rsqb;\tilde{H}\&lsqb;l+M_1{D}_t-i,k_m\&rsqb;,$ Wherein subscript ^*represent conjugation; for tap coefficient, obtain when (n-1)th training pilot tone training; n _f=Q _td _t+ 1, Q _t=M ₁+ M ₂+ 1, M ₁, M ₂for the parameter arranged, M ₁>=0, M ₂+ 1>=1; Work as l+M ₁d _t-i, 0≤i≤N _fwhen the OFDM symbol of-1 correspondence does not exist,

S5.3: upgrade tap coefficient ${\hat{w}}_i\&lsqb;n+1\&rsqb;={\hat{w}}_i\&lsqb;n\&rsqb;+\mathrm{\&rho;}\tilde{H}\&lsqb;l+M_1{D}_t-i,k_m\&rsqb;e_l^{*}\&lsqb;n\&rsqb;,i=0,...,N_f-1,$ Wherein ρ is default step-length, be the tap coefficient of (n+1)th training pilot tone, when first time carries out data channel estimation to OFDM symbol, train tap coefficient corresponding to pilot tone to be 0 for its 0th; Work as N _lwhen individual training pilot tone has all been trained, export interpolation coefficient $c_l\&lsqb;j\&rsqb;={\hat{w}}_{j+M_1{D}_t}^{*}\&lsqb;\underset{y=0}{\overset{l}{\&Sigma;}}{N}_y\&rsqb;,j=-M_1{D}_t,-M_1{D}_t+1,...,(M_2+1)D_t-1;$

S5.4: according to interpolation coefficient c _l[j], the channel estimation value calculating data in l OFDM symbol is: k _dit is the subcarrier that in l OFDM symbol, data are corresponding.

2. OFDM adaptive channel estimation method according to claim 1, is characterized in that, trains pilot tone random distribution in OFDM symbol in described step S2, and its random rule is known to receiving terminal.

3. OFDM adaptive channel estimation method according to claim 1, is characterized in that, trains pilot tone to be known training information in described step S2.

4. OFDM adaptive channel estimation method according to claim 1, is characterized in that, trains pilot tone to be produced by decision-feedback in described step S2.

5. OFDM adaptive channel estimation method according to claim 1, is characterized in that, estimates that the method for channel frequency domain response is LS algorithm or MMSE algorithm in described step S3.

6., according to the arbitrary described OFDM adaptive channel estimation method of claim 1 to 5, it is characterized in that, in described step S5, initial OFDM symbol is (M ₂+ 1) D _tindividual OFDM symbol.

7. OFDM adaptive channel estimation method according to claim 6, is characterized in that, described (M ₂+ 1) D _tindividual OFDM symbol and OFDM symbol before thereof do not carry uses data message.